Deep Learning Foundation: Linear Regression and Statistics (Udemy)
Deep Learning Foundation: Linear Regression and Statistics (Udemy) ranks first in the list of best online Linear Regression course. Linear regression is a good place to start in data science, and this course will help you build a solid foundation for deep learning and machine learning algorithms. This course covered hypothesis testing, unbiased estimators, statistical tests, and gradient descent. By the end of the course, you will be able to write your own regression algorithm from the ground up.
This course will help you gain a solid understanding of the fundamental topics that form the foundation of artificial intelligence and its sub-areas. Begin with introductory terminology, then move on to a statistical test, gradient descent, and logistic fitting. After completing this project, you will be able to design and implement regression algorithms. Take a look at their list of the Best Linear Algebra Courses.
Highlights:
- Develop a model from scratch with real-world examples
- Learn about cost optimization and adaptive learning rate
- Discuss sample estimator, distributions, and hypothesis testing
- Look into additional mathematical and Python programming concepts in the bonus section
- Touch upon important data science interview questions
- 44 Lectures + 3 Downloadable resources + Full lifetime access
What you will learn:
- Mathematics behind R-Squared, Linear Regression,VIF and more!
- Deep understating of Gradient descent and Optimization
- Program your own version of a linear regression model in Python
- Derive and solve a linear regression model, and implement it appropriately to data science problems
- Statistical background of Linear regression and Assumptions
- Assumptions of linear regression hypothesis testing
- Writing codes for T-Test, Z-Test and Chi-Squared Test in python
Duration: 6.5 hours
Rating: 4.9 out of 5
Enroll here: udemy.com/course/linear-regression-in-python-statistics-and-coding
